The embodiments herein relate generally to systems and processes for generating a user based virtual recommendation from user contact sources.
With the advent of online communication, consumers rely on others' opinions to help determine whether a product or service should be purchased. Conventionally, people rely on others' reviews, ratings, or positive indicators (for example, a “thumbs up” rating). First, when reviewing a product or service online, the current systems or methodologies provides an output which is a list in which a person can read feedback from a customer one by one (a review), view a list ranking of previous customers ratings from best to worst, view a list ranking of products or services from best to worst (or recommended products or services), or merely just view a numerical total number of positive indications (e.g., the number of thumbs up). Such conventional approaches are discrete with the systems working independently from each other. A problem with discrete systems is one person can have many ways in which he/she has communicated an opinion about something. To illustrate, one person can rate a product, they can then write a review, and then give a thumbs up. One day, the person may have a good experience and give a thumbs up, a year later a bad experience and give a poor rating, and a year later after that have an average experience and write an average review. This is a problem for a consumer looking for a reference because there are now three references on three systems from the same person that may not reflect an accurate and current view of the experience. The current system approaches are largely “disaggregated” since they are discrete “events” independent of each other. Second, the current system approaches are commonly a compilation of submissions from a variety of uncontrolled sources. Uncontrolled sources present problems as it is becoming more clear that reviews, ratings, and thumbs up can be purchased and performed by professional “raters”, which reduces “reliability” in the rating or review. For example, the current system allows a user to write a review or provide a rating and in most cases it is not clear or even checked if the person has in fact ever used the product or service. It some cases, current online approaches to providing consumer opinion on a product/service can be manipulated by individuals and computing “bots” to provide unreliable opinions (either good or bad) or can be skewed by redundant input from the same previous consumer. Third, current approaches are largely static at a point in time and lack a means to update. A user writes a review and that review is for a service provided at a point in time and generally not amended based on future reviews. Therefore, these systems are less reliable because many reviews from the same person over a period of time become “dilutive” to the overall rating system. Fourth, the current systems do not even consider non user input behavioral implied ratings. An example of this is a repeat customer. A repeat customer is the best indication of a happy customer.
As can be seen, there is a need for a system that uses an integrated approach to incorporating the many ways in which people rate, review or otherwise recommend a product or service and improves the reliability of a recommendation.